Intelligent control method for dynamic neural network-based variable cycle engine

ABSTRACT

An intelligent control method for a dynamic neural network-based variable cycle engine is provided. By adding a grey relation analysis method-based structure adjustment algorithm to the neural network training algorithm, the neural network structure is adjusted, a dynamic neural network controller is constructed, and thus the intelligent control of the variable cycle engine is realized. A dynamic neural network is trained through the grey relation analysis method-based network structure adjustment algorithm designed by the present invention, and an intelligent controller of the dynamic neural network-based variable cycle engine is constructed. Thus, the problem of coupling between nonlinear multiple variables caused by the increase of control variables of the variable cycle engine and the problem that the traditional control method relies too much on model accuracy are effectively solved.

Technical Field

The present invention belongs to the technical field of control ofaero-engines, and particularly relates to an intelligent control methodfor a dynamic neural network-based variable cycle engine.

Background

As an important part of an aero-engine, the control system is a keyfactor affecting the performance of the aero-engine. The variable cycleengine is integrated with the advantages of the turbojet engine andturbofan engine, and is added with variable geometry components. Byadaptively adjusting the geometry, position and other parameters ofthese components, the variable cycle engine can have high specificthrust in the maneuvering flight phases of the aircraft such asacceleration, ascending and the like; and can have low fuel consumptionrate in the subsonic cruise phase. However, due to the increase ofvariable components, the control variables are increased, and thestructure of the controller is made more complicated; meanwhile, astrong coupling relationship is made between the nonlinear controlvariables of the variable cycle engine. How to solve the problem ofcoupling between control loops so that various variables may cooperatewith each other to give full play to the overall performance and realizethe multi-variable control of the variable cycle engine is a problemthat is to be solved urgently, and is also the basis for the subsequentdevelopment of transient state control and synthesis control in the fullflight envelop.

At present, for most of the multi-variable control systems for variablecycle engines at home and abroad, genetic algorithms (GA),teaching-learning-based optimization algorithms (TLBO) and otherintelligent optimization algorithms are added or compensators are addedon the basis of the traditional robust control, linear quadraticregulator (LQR) control and the like. Most of the existing controlmethods are based on accurate engine models, so the accuracy of themodels is made to directly affect the control effect of the controller,and the problem of coupling between nonlinear variables cannot be solvedwell. Due to the nonlinear characteristics of the neural network, theneural network-based controller can well solve the problem of couplingbetween multiple variables. However, during training, the structure ofhidden layers cannot be determined directly, if the structure is toolarge, too slow training and overfitting may be caused, and if thestructure is too small, the required accuracy cannot be achieved.Therefore, in view of the above problem, it is of great significance todesign a dynamic neural network controller which can overcome thestructure defect of the neural network and is used for control of avariable cycle engine.

Summary

In view of the problem existing in the existing method formulti-variable control of the variable cycle engine, the presentinvention provides an intelligent control method for a dynamic neuralnetwork-based variable cycle engine. By adding a grey relation analysismethod-based structure adjustment algorithm to the neural networktraining algorithm, the neural network structure is adjusted, a dynamicneural network controller is constructed, and thus the intelligentcontrol of the variable cycle engine is realized.

The technical solution of the present invention is as follows:

An intelligent control method for a dynamic neural network-basedvariable cycle engine, comprising the following steps:

Step 1: constructing a training dataset of a dynamic neural network

Step 1.1: taking 0.01 s as a sample period, collecting the operatingparameters of the variable cycle engine at the set height and powerlevel angle(PLA), including an actual value and a desired value of arelative rotary speed of high pressure rotor n_(h) of a controlledvariable, a desired value and an actual value of a pressure ratio π andcontrol variables, wherein the control variables include the actualoperating values of fuel flow W_(ƒf), nozzle throat area A₈,high-pressure turbine vane area A_(HTG), angle of outlet guide vaneα_(ƒ), angle of core driven fan guide vane α_(CDFS), angle of compressorguide vane α_(c), angle of high-pressure compressor guide vane α_(turb),and core driven fan mixer area A_(CDFS);

Step 1.2: performing data processing on the operating parameters of thevariable cycle engine collected in the step 1.1, and deleting anoutlier, i.e. exception value; and for a missing value, performing meaninterpolation of the same class;

Step 1.3: taking the deviation Δn_(h) between the desired value and theactual value of the relative rotary speed of high pressure rotor n_(h)in the operating parameters of the variable cycle engine on which dataprocessing is performed and the deviation Δπ between the desired valueand the actual value of the pressure ratio π of the engine as inputparameters of the dynamic neural network, and taking the change value ofthe control variables as the target output of the dynamic neuralnetwork;

constructing a training dataset of the dynamic neural network:

-   -   x=[Δn_(h), Δπ]    -   Y=[W_(ƒ), α_(turb), α_(ƒ), A₈, α_(CDFS), A^(HTG), α_(c)]    -   h=[x, y]        where x represents the input parameter of the dynamic neural        network, Y represents the target output of the dynamic neural        network, and h represents the training dataset of the dynamic        neural network;

Step 1.4: normalizing the training dataset of the dynamic neuralnetwork:

$h_{norm} = \frac{h - h_{\min}}{h_{\max} - h_{\min}}$where h_(norm), h_(min) and h_(max) respectively represent datanormalized value of the training dataset h of the dynamic neuralnetwork, minimum value and maximum value;

Step 2: training the dynamic neural network

Step 2.1: taking 80% of the normalized training dataset h_(norm) of thedynamic neural network obtained in the step 1.4 as the training sample,and 20% as the test sample;

Step 2.2: initializing the parameters of the dynamic neural network:number of neurons in an input layer, number of neurons in all hiddenlayers, number of neurons in an output layer, iteration number, trainingaccuracy ε and learning rate μ;

Step 2.3: calculating the state and activation value of each layer ofthe dynamic neural network:

$\left\{ \begin{matrix}{{{a^{(l)}(k)} = {x(k)}},{l = 1}} \\{{{a^{(l)}(k)} = {f\left( {z^{(l)}(k)} \right)}},{2 \leq l \leq L}}\end{matrix} \right.$z^((l))(k) = Θ^((l))(k)a^((l − 1))(k) + b^((l))(k), 2 ≤ l ≤ Lwhere L represents the total number of layers of the dynamic neuralnetwork, ƒ(·) represents the activation function of the neuron;Θ^((l))(k)∈R^(n) ^(l) ^(×x) ^(n−1) represents the weight matrix of thel−1^(th) layer to the l^(th) layer when learning the input x(k) of thek^(th) sample; n_(l) represents the number of neurons in the l^(th)layer;

b^((l))(k)=(b^((l)) ₁(k), b^((l)) ₂(k), . . . , b^((l)) _(n) _(l)(k))^(T)∈R^(n) ^(l) represents the bias of the l−1^(th) layer to thel^(th) layer when the input is x(k);

z^((l))(k)=(z^((l)) ₁(k), z^((l)) ₂(k), . . . , z^((l)) _(n) _(l)(k))^(T)∈R^(n) ^(l) represents the state of the neurons in the l^(th)layer when the input is x(k); and

α^((l))(k)=(α^((l)) ₁(k), α^((l)) ₂(k), . . . , α^((l)) _(n) _(l)(k))_(T)∈R^(n) ^(l) represents the activation value, i.e. output value,of the neurons in the l^(th) layer when the input is x(k);

Step 2.4: calculating the error and loss of the output layer of thedynamic neural network:

e(k) = y(k) − o(k) ${{E(k)} = {\frac{1}{2}{{e(k)}}^{2}}}$

where y(k) represents the desired output of the k^(th) sample, o(k)represents the actual output generated by the dynamic neural network forthe input x(k) of the k^(th) sample, e(k) represents the error of theoutput layer at this time, E(k) represents the loss of the output layerwhen learning the k^(th) sample currently, and

▯represents the norm operation;

calculating δ_((l)) as the intermediate quality of error backpropagation:

$\left\{ \begin{matrix}{{{\delta^{(l)}(k)} = {{\delta^{({l + 1})}(k)}{\Theta^{({l + 1})}(k)}{a^{(l)}(k)}\left( {1 - {a^{(l)}(k)}} \right)}},{2 \leq l < L}} \\{{{\delta^{(l)}(k)} = {{- \left( {{y(k)} - {a^{(L)}(k)}} \right)}{a^{(L)}(k)}\left( {1 - {a^{(L)}(k)}} \right)}},{l = L}}\end{matrix} \right.$

updating the weight matrix and bias parameter matrix of the dynamicneural network:Θ^((l))(k+1)=Θ^((l))(k)−μδ^((l) () k)α^((l−1))(k)b ^((l))(k+1)=b ^((l))(k)−μδ^((l))(k)

Step 2.5: performing structure increase judgment of the dynamic neuralnetwork calculating the average loss

${E:E} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}{E(k)}}}$of all samples after training once, where N represents the total numberof samples; calculating the average value

$\frac{1}{N_{w}}{\sum\limits_{i = 1}^{N_{w}}E_{i}}$of the loss of the output layer in a current sample window, where N_(w)represents the size of a sliding window;

if E_(i)>ε is met after the i^(th) training, adding neurons from thehidden layer 1, i.e. layer 2 of the dynamic neural network, 1=2;initializing the weight θ_(new) of the newly added neurons with a randomnumber, and updating the number of neurons in the l^(th) layer of thedynamic neural network and the weight matrix during the i+1^(th)training:n _(i)(i+1)=n_(l)(i)+1Θ^((l))(i+1)=[Θ^((l))(i), θ_(new)]after adding neurons to the current l^(th) layer (1<l<L), if aftercontinuous N_(w) training, the loss change rate of the output layer ofthe dynamic neural network is less than the threshold

${\eta_{3}:\frac{1}{N_{w}}\left( {E_{({i - {Nw}})} - E_{i}} \right)} < \eta_{3}$and E_(i)>ε is met, going to the next hidden layer for neuron increasejudgment, and ending structure increase judgment until l=L;

Step 2.6: after stopping adding neurons to the hidden layers of thedynamic neural network, performing structure pruning judgment; takingthe hidden layer node output α^((l))=(α^((l)) ₁, α^((l)) ₂, . . . ,α^((l)))^(T)∈R^(n) ^(l) as a comparison serial and the dynamic neuralnetwork output o=(o₁, o², . . . , o_(nL)) as a reference serial,calculating a correlation coefficient ξ^(l) _(n) betweenα^((l))=(α^((l)) ₁, α^((l)) ₂, . . . , α^((l)) _(n) _(l) )^(T)∈R^(n)^(l) and o=(o₁, o₂, . . . , o_(nL)):

${\xi_{n}^{l}(k)} = \frac{{\underset{n}{\min}\min\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}} + {\rho\max\limits_{n}\max\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}{{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘} + {\rho\max\limits_{n}\max\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}$where ρ∈(0, +∞) represents a distinguishing coefficient, and ξ^(l)_(n)(k)(k=1, 2, . . . , N) represents a grey relation serial;

calculating the relativity using the average value method,

γ n l = 1 N ⁢ ∑ k = 1 N ξ n l ( k )

where γ^(l) ₂∈[0,1] represents the relativity; comparing the relativityγ¹ _(n) between various neurons in the l^(th) layer, deleting the neuronwith the minimum relativity, updating the number n_(l) of the neurons inthe l^(th) layer of the dynamic neural network and the weight matrixΘ^((l);)

if the loss of the output layer of the dynamic neural network isincreased too much so that E_(i+1)−E_(i)>η₄ after a certain neuron inthe l^(th) layer (1<l<L) is deleted, where η₄>0 represents the errorfloating threshold, withdrawing the deleting operation in the previousstep and going to the structure adjustment judgment of the next hiddenlayer, and ending structure pruning judgment until l=L;

Step 2.7: if the error meets a given accuracy requirement, ending thetraining, and saving the weight matrix and bias parameter matrix of thedynamic neural network; and if the error does not meet the givenaccuracy requirement, proceeding the iterative training until theaccuracy requirement is met or the specified iteration number isreached;

Step 2.8: based on the test sample, checking the currently traineddynamic neural network, and calculating the test error;

Step 3: constructing a dynamic neural network controller encapsulatingthe trained dynamic neural network as a variable cycle enginecontroller, and normalizing the deviation Δn_(h) between the desiredvalue and the actual value of the relative rotary speed of high pressurerotor n_(h) of the controlled variable and the deviation Δπ between thedesired value and the actual value of the pressure ratio π of the engineand then taking same as input parameters of the dynamic neural networkcontroller; and inversely normalizing the output parameters of thedynamic neural network and then taking same as the control variables ofthe variable cycle engine, to realize the intelligent control of thevariable cycle engine.

The present invention has the advantageous effects that: a dynamicneural network is trained through the grey relation analysismethod-based network structure adjustment algorithm designed by thepresent invention, and an intelligent controller of the dynamic neuralnetwork-based variable cycle engine is constructed. The problem ofcoupling between nonlinear multiple variables caused by the increase ofcontrol variables of the variable cycle engine and the problem that thetraditional control method relies too much on model accuracy areeffectively solved. Meanwhile, the structure is dynamically adjusted inthe training process of the neural network, so that the networkstructure is simpler, and the operating speed and control accuracy areincreased.

DESCRIPTION OF DRAWINGS

FIG. 1 shows a flow chart of a dynamic structure adjustment algorithm ofa dynamic neural network.

FIG. 2 shows a structural schematic diagram of the used dynamic neuralnetwork.

FIG. 3(a), FIG. 3(b) and FIG. 3(c) show change curves of the number ofneurons in all hidden layers in the training process when using themethod proposed by the present invention in the case where the initialheight of the variable cycle engine is 0, the initial Mach number is 0,the PLA is 50°, when the number of the hidden layers of the dynamicneural network is 3, and the initial number of neurons in all layers is10, 5, 5 respectively.

FIG. 4 shows an error change curve in the training process when usingthe method proposed by the present invention in the case where theinitial height of the variable cycle engine is 0, the initial Machnumber is 0, the PLA is 50°, when the number of the hidden layers of thedynamic neural network is 3, and the initial number of neurons in alllayers is 10, 5, 5 respectively.

FIG. 5 is a block diagram of a closed-loop control system of the dynamicneural network of the variable cycle engine.

FIG. 6(a) and FIG. 6(b) are diagrams showing the effect of tracking thetarget value of the relative rotary speed of high pressure rotor of0.9622 and the target value of the pressure ratio of 3.6 by means of thedynamic neural network controller in the case where the initial heightof the variable cycle engine is 0, the initial Mach number is 0, and thePLA is 50°.

FIG. 7(a) and FIG. 7(b) are comparison diagrams showing the effect ofcontrolling the target value of the relative rotary speed of highpressure rotor of 0.9622 and the target value of the pressure ratio of3.6 by means of the dynamic neural network controller and the neuralnetwork controller of the fixed structure in the case where the initialheight of the variable cycle engine is 0, the initial Mach number is 0,and the PLA is 50°.

DETAILED DESCRIPTION

The embodiments of the present invention will be further described indetail below in combination with the drawings and the technicalsolution.

The method of the present invention comprises the following specificsteps:

Step 1: constructing a training dataset of a dynamic neural network.

Step 1.1: taking 0.01 s as a sample period, collecting the operatingparameters of the variable cycle engine at the set height and PLA,including an actual value and a desired value of a relative rotary speedof high pressure rotor n_(h) of a controlled variable, a desired valueand an actual value of a pressure ratio π, and control variables,wherein the control variables include the actual operating values offuel flow W_(ƒ), nozzle throat area A₈, high-pressure turbine vane areaA_(HTG), angle of outlet guide vane α_(ƒ), angle of core driven fanguide vane α_(CDFS), angle of compressor guide vane α_(c), angle ofhigh-pressure compressor guide vane α_(turb), and core driven fan mixerarea A_(CDFS);

Step 1.2: performing data processing on the operating parameters of thevariable cycle engine collected in the step 1.1, and deleting an outlier(exception value); and for a missing value, performing meaninterpolation of the same class.

Step 1.3: taking the deviation Δn_(h) between the desired value and theactual value of the relative rotary speed of high pressure rotor n_(h)in the operating parameters of the variable cycle engine on which dataprocessing is performed and the deviation Δπ between the desired valueand the actual value of the pressure ratio π of the engine as inputparameters of the dynamic neural network, and taking the change value ofthe control variables as the target output of the dynamic neuralnetwork;

constructing a training dataset of a dynamic neural network:

-   -   x=[Δn_(h), Δπ]    -   y=[W_(ƒ), α_(turb), α_(ƒ), A₈, α_(CDFS), A_(CDFS), A_(HTG),        α_(c)]    -   h=[x, y]        where x represents input parameter of the dynamic neural        network, Y represents target output of the dynamic neural        network, and h represents training dataset of the dynamic neural        network.

Step 1.4: normalizing the training dataset of the dynamic neuralnetwork:

$h_{norm} = \frac{h - h_{\min}}{h_{\max} - h_{\min}}$where h_(norm), h_(min) and h_(man) respectively represent datanormalized value of the training dataset h of the dynamic neuralnetwork, minimum value and maximum value.

As shown in FIG. 1 , in the training process of the dynamic neuralnetwork, the steps of the structure adjustment algorithm are as follows.

The structural schematic diagram of the used dynamic neural network isshown in FIG. 2 .

Step 2: training the dynamic neural network.

Step 2.1: taking 80% of the normalized training dataset h_(norm) of thedynamic neural network obtained in the step 1.4 as the training sample,and 20% as the test sample.

Step 2.2: initializing the training parameters of the dynamic neuralnetwork.

The number of neurons in the input layer is 2, the number of neurons inthe output layer is 8, the hidden layer structure is initialized to 10,5, 5 (three hidden layers, the number of neurons in all the layers is10, 5, 5 respectively), the learning rate is μ=0.1, the iteration numberis 850, the accuracy requirement is ε=1.0e−3. Θ^((l))∈R^(n) ^(l) ^(×n)^(l−1) (2≤l≤L) represents the weight matrix of the l−1^(th) layer to thel^(th) layer, initialized as a random number matrix with the valuebetween [0, 1], and b^((l))=(b^((l)) ₁, b^((l)) ₂, . . . , b^((l)) _(n)_(l) )^(T)∈R^(n) ^(l) represents the bias of the l−1^(th) layer to thel^(th) layer, initialized as a random number matrix with the valuebetween [0, 1].

-   -   Θ⁽²⁾:[2×10 double],b⁽²⁾:[1×10 double]    -   Θ⁽³⁾:[10×5 double],b⁽³⁾:[1×5 double]    -   Θ⁽⁴⁾:[5×5 double],b⁽⁴⁾:[1×5 double]    -   Θ⁽⁵⁾:[5×8 double],b⁽⁵⁾:[1×8 double]

Step 2.3: calculating the state and activation value of each layer ofthe dynamic neural network:

$\left\{ \begin{matrix}{{{a^{(l)}(k)} = {x(k)}},{l = 1}} \\{{{a^{(l)}(k)} = {f\left( {z^{(l)}(k)} \right)}},{2 \leq l \leq L}}\end{matrix} \right.$z^((l))(k) = Θ^((l))(k)a^((l − 1))(k) + b^((l))(k), 2 ≤ l ≤ Lwhere L=5 represents the total number of layers of the dynamic neuralnetwork, ƒ(·) represents the activation function of the neuron, which isa sigmoid function; Θ^((l))(k)∈R^(n) ^(l) ^(×n) ^(l−1) represents theweight matrix of the l−1^(th) layer to the l^(th) layer when learningthe input x(k) of the k^(th) sample; n_(l) represents the number ofneurons in the l^(th) layer;

b^((l))(k)=(b^((l)) ₁(k), b^((l)) ₂(k), . . . , b^((l)) _(n) _(l)(k))^(T)∈R^(n) ^(l) represents the bias of the l−1^(th) layer to thel^(th) layer when the input is x(k);

z^((l))(k)=(z^((l)) ₁(k), z^((l)) ₂(k), . . . , z^((l)) _(n) _(l)(k))^(T)∈R^(n) ^(l) represents the state of the neurons in the l^(th)layer when the input is x(k); and

α^((l))(k)=(α^((l)) ₁(k), α^((l)) ₂(k), . . . , α^((l)) _(n) _(l)(k))^(T)∈R^(n) ^(l) represents the activation value, i.e. output value,of the neurons in the l^(th) layer when the input is x(k).

Step 2.4: calculating the error and loss of the output layer of thedynamic neural network:

e(k) = y(k) − o(k) ${E(k)} = {\frac{1}{2}{{e(k)}}^{2}}$where y(k) represents the desired output of the k^(th) sample, o(k)represents the actual output generated by the dynamic neural network forthe input x(k) of the k^(th) sample, e(k) represents the error of theoutput layer at this time, E(k) represents the loss of the output layerwhen learning the k^(th) sample currently, and

▯represents the norm operation;

calculating δ^((l)) as the intermediate quality of error backpropagation:

$\begin{matrix}\left\{ \begin{matrix}{{{\delta^{(l)}(k)} = {{\delta^{({l + 1})}(k)}{\Theta^{({l + 1})}(k)}{a^{(l)}(k)}\left( {1 - {a^{(l)}(k)}} \right)}},{2 \leq l < L}} \\{{{\delta^{(l)}(k)} = {{- \left( {{y(k)} - {a^{(L)}(k)}} \right)}{a^{(L)}(k)}\left( {1 - {a^{(L)}(k)}} \right)}},{l = L}}\end{matrix} \right. & \end{matrix}$updating the weight matrix and bias parameter matrix of the dynamicneural network:Θ^((l))(k+1)=Θ^((l))(k)−μδ^((l))(k)α^((l−1))(k)b ^((l))(k+1)=b ^((l))(k)−μδ^((l))(k)

Step 2.5: performing structure increase judgment of the dynamic neuralnetwork calculating the average loss

${E\text{:}E} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}{E(k)}}}$of all samples after training once, where N represents the total numberof samples; calculating the average value

$\frac{1}{N_{w}}{\sum\limits_{i = 1}^{N_{w}}E_{i}}$of the loss of the output layer in a current sample window, whereN_(w)=10 represents the size of a sliding window;

if E_(i)>ε is met after the i^(th) training, adding neurons from thehidden layer 1, i.e. layer 2 of the dynamic neural network, 1=2;initializing the weight θ_(new) of the newly added neurons with a randomnumber, and updating the number of neurons in the l^(th) layer of thedynamic neural network and the weight matrix during the i+1^(th)training:n _(l)(i+1)=n _(l)(i)+1Θ^((l))(i+1)=[Θ^((l))(i), θ_(new)]after adding neurons to the current l^(th) layer (1<l<L), if aftercontinuous N_(w) training, the loss change rate of the output layer ofthe dynamic neural network is less than the threshold

${\eta_{3}\text{:}\frac{1}{N_{w}}\left( {E_{({i - {Nw}})} - E_{i}} \right)} < \eta_{3}$and E_(i)>ε is met, going to the next hidden layer for neuron increasejudgment, and ending structure increase judgment until l=L.

Step 2.6: after stopping adding neurons to the hidden layers of thedynamic neural network, performing structure pruning judgment; takingthe hidden layer node output α^((l))=(α^((l)) ₁, α^((l)) ₂, . . . ,α^((l)) _(n) _(l) )^(T)∈R^(n) ^(l) as a comparison serial and thedynamic neural network output o=(o₁, o₂, . . . , o_(nL)) as a referenceserial, calculating a correlation coefficient ξ^(l) _(n) betweenα^((l))=(α^((l)), α^((l)) ₂, . . . , α^((l)) _(n) _(l) )^(T)∈R^(n)^(l and o=(o) ₁, o₂, . . . , o_(nL)):

$\begin{matrix}{{\xi_{n}^{l}(k)} = \frac{{\min\limits_{n}\min\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}} + {\rho\max\limits_{n}\max\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}{{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘} + {\rho\max\limits_{n}\max\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}} & \end{matrix}$where ρ=0.15 represents a distinguishing coefficient, and ξ¹_(b)(k)(k=1, 2, . . . , N) represents a grey relation serial;

calculating the relativity using the average value method:

$\gamma_{n}^{l} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}{\xi_{n}^{l}(k)}}}$

where γ^(l) _(n)∈[0,1] represents the relativity; comparing therelativity γ^(l) _(n) between various neurons in the l^(th) layer,deleting the neuron with the minimum relativity, updating the numbern_(l) of the neurons in the l^(th) layer of the dynamic neural networkand the weight matrix Θ^((l));

if the loss of the output layer of the dynamic neural network isincreased too much so that E_(i+1)−E_(i)>η₄ after a certain neuron inthe l^(th) layer (1<l<L) is deleted, where η₄>0 represents the errorfloating threshold, withdrawing the deleting operation in the previousstep and going to the structure adjustment judgment of the next hiddenlayer, and ending structure pruning judgment until l=L.

Step 2.7: if the error meets a given accuracy requirement, ending thetraining, and saving the weight matrix and bias parameter matrix of thedynamic neural network; and if the error does not meet the givenaccuracy requirement, proceeding the iterative training until theaccuracy requirement is met or the specified iteration number isreached.

After the training, the hidden layer structure of the dynamic neuralnetwork is finally 9, 4, and 14. The changes of neurons of all hiddenlayers in the training process of the dynamic neural network are shownin FIG. 3(a), FIG. 3(b), and FIG. 3(c).

The error change curve in the training process of the dynamic neuralnetwork is shown in FIG. 4 .

Step 2.8: based on the test sample, checking the currently traineddynamic neural network, and calculating the test error, the test errorbeing 8.89e-04.

Step 3: constructing a dynamic neural network controller. As shown inFIG. 5 , encapsulating the trained dynamic neural network as a variablecycle engine controller, and normalizing the deviation Δn_(h) betweenthe desired value and the actual value of the relative rotary speed ofhigh pressure rotor n_(h) of the controlled variable and the deviationΔπ between the desired value and the actual value of the pressure ratioπ of the engine and then taking same as input parameters of the dynamicneural network controller; and inversely normalizing the outputparameters of the dynamic neural network and then taking same as thecontrol variables of the variable cycle engine, to realize theintelligent control of the variable cycle engine.

FIG. 6(a) and FIG. 6(b) are diagrams showing the effect of tracking thetarget value of the relative rotary speed of high pressure rotor of0.9622 and the target value of the pressure ratio of 3.6 by means of thedynamic neural network controller in the case where the initial heightof the variable cycle engine is 0, the initial Mach number is 0, and thePLA is 50°.

It can be seen from the simulation results of FIG. 6(a) and FIG. 6(b)that the dynamic neural network controller designed using the methodproposed by the present invention can make the control accuracy of therelative rotary speed of high pressure rotor reach 99.999% and thecontrol accuracy of the pressure ratio reach 99.713%.

FIG. 7(a) and FIG. 7(b) are comparison diagrams showing the effect ofcontrolling the target value of the relative rotary speed of highpressure rotor of 0.9622 and the target value of the pressure ratio of3.6 by means of the dynamic neural network controller and the neuralnetwork controller of the fixed structure in the case where the initialheight of the variable cycle engine is 0, the initial Mach number is 0,and the PLA is 50°.

As shown in FIG. 7(a) and FIG. 7(b), compared with the neural networkcontroller of the fixed structure, the control error of the dynamicneural network controller for the rotary speed of high pressure rotor isreduced by 97.47%; and compared with the neural network controller ofthe fixed structure, the control error of the dynamic neural networkcontroller for the pressure ratio is reduced by 90.10%.

In summary, in the control of the variable cycle engine, the dynamicneural network trained thought the grey relation analysis method-baseddynamic structure adjustment algorithm is used to construct the dynamicneural network controller of the variable cycle engine, so the networkstructure is simpler, the control accuracy is higher, and the time toreach steady state is faster.

The invention claimed is:
 1. An intelligent control method for a dynamicneural network-based variable cycle engine, comprising the followingsteps: Step 1: constructing a training dataset of a dynamic neuralnetwork: Step 1.1: taking 0.01 s as a sample period, collectingoperating parameters of a variable cycle engine at a set height andpower level angle (PLA), including an actual value and a desired valueof a relative rotary speed of high pressure rotor n_(h) of a controlledvariable, a desired value and an actual value of a pressure ratio π, andcontrol variables, wherein the control variables include actualoperating values of fuel flow W_(ƒ), nozzle throat area A₈,high-pressure turbine vane area A_(HTG), angle of outlet guide vaneα_(ƒ), angle of core driven fan guide vane α_(CDFS), angle of compressorguide vane α_(c), angle of high-pressure compressor guide vane α_(turb),and core driven fan mixer area A_(CDFS); Step 1.2: performing dataprocessing on the operating parameters of the variable cycle enginecollected in step 1.1, and deleting an outlier, i.e., exception value;and for a missing value, performing mean interpolation of a same class;Step 1.3: taking a deviation Δn_(h), between the desired value and theactual value of the relative rotary speed of high pressure rotor n_(h)in the operating parameters of the variable cycle engine on which dataprocessing is performed and a deviation Δπ between the desired value andthe actual value of the pressure ratio π of the variable cycle engine asinput parameters of the dynamic neural network, and taking a changevalue of the control variables as a target output of the dynamic neuralnetwork; constructing a training dataset of the dynamic neural network:x=[Δn_(h), Δπ] y=[W_(ƒ), α_(turb), α_(ƒ), A₈, α_(CDFS), A_(CDFS),A_(HTG), α_(c)] h=[x, y] where x represents the input parameters of thedynamic neural network, y represents the target output of the dynamicneural network, and h represents the training dataset of the dynamicneural network; Step 1.4: normalizing the training dataset of thedynamic neural network:$h_{norm} = \frac{h - h_{\min}}{h_{\max} - h_{\min}}$ where h_(norm),h_(min) and h_(max) respectively represent data normalized value of thetraining dataset h of the dynamic neural network, minimum value andmaximum value; Step 2: training the dynamic neural network: Step 2.1:taking 80% of data normalized value h_(norm) of training dataset h ofthe dynamic neural network obtained in step 1.4 as a training sample,and 20% as a test sample; Step 2.2: initializing parameters of thedynamic neural network: number of neurons in an input layer, number ofneurons in all hidden layers, number of neurons in an output layer,iteration number, training accuracy ε and learning rate μ; Step 2.3:calculating a state and activation value of each layer of the dynamicneural network: $\left\{ \begin{matrix}{{a^{(l)} = {x(k)}},{l = 1}} \\{{a^{(l)} = {f\left( {z^{(l)}(k)} \right)}},{2 \leq l \leq L}}\end{matrix} \right.$z^((l))(k) = Θ^((l))(k)a^((l − 1))(k) + b^((l))(k), 2 ≤ l ≤ L where Lrepresents a total number of layers of the dynamic neural network, ƒ(·)represents an activation function of a neuron; Θ^((l))(k)∈R^(n) ^(l)^(×n) ^(l−1) represents a weight matrix of the l−1^(th) layer to thel^(th) layer when learning an input x(k) of a k^(th) sample; n_(l)represents a number of neurons in the l^(th) layer; b^((l))(k)=(b^((l))₁(k), b^((l)) ₂(k), . . . , b^((l)) _(n) _(l) (k))^(T)∈R^(n) ^(l)represents a bias of the l−1^(th) layer to the l^(th) layer when theinput is x(k); z^((l))(k)=(z^((l)) ₁(k), z^((l)) ₂(k), . . . , z^((l))_(n) _(l) (k))^(T)∈R^(n) ^(l) represents a state of the neurons in thel^(th) layer when the input is x(k); and α^((l))(k)=(α^((l)) ₁(k),α^((l)) ₂(k), . . . , α^((l)) _(n) _(l) (k))^(T)∈R^(n) ^(l) representsan activation value, i.e., output value, of the neurons in the l^(th)layer when the input is x(k); Step 2.4: calculating an error and loss ofthe output layer of the dynamic neural network: e(k) = y(k) − o(k)${E(k)} = {\frac{1}{2}{{e(k)}}^{2}}$ where y(k) represents a desiredoutput of the k^(th) sample, o(k) represents an actual output generatedby the dynamic neural network for the input x(k) of the k^(th) sample,e(k) represents the error of the output layer at this time, E(k)represents the loss of the output layer when learning the k^(th) samplecurrently, and ∥∥ represents the norm operation; calculating δ^((l)) asan intermediate quality of error back propagation: $\begin{matrix}\left\{ \begin{matrix}{{{\delta^{(l)}(k)} = {{\delta^{({l + 1})}(k)}{\Theta^{({l + 1})}(k)}{a^{(l)}(k)}\left( {1 - {a^{(l)}(k)}} \right)}}\ ,{2 \leq l < L}} \\{{{\delta^{(l)}(k)} = {{- \left( {{y(k)} - {a^{(L)}(k)}} \right)}{a^{(L)}(k)}\left( {1 - {a^{(L)}(k)}} \right)}}\ ,{l = L}}\end{matrix} \right. & \end{matrix}$ updating the weight matrix and bias parameter matrix ofthe dynamic neural network:Θ^((l))(k+1)=Θ_((l))(k)−μδ^((l))(k)α^((l−1))(k)b ^((l))(k+1)=b ^((l))(k)−μδ^((l))(k) Step 2.5: performing structureincrease judgment of the dynamic neural network: calculating an averageloss ${E\text{:}E} = {\frac{1}{N}{\sum}_{k = 1}^{N}{E(k)}}$ of allsamples after training once, where N represents a total number ofsamples; calculating an average value$\frac{1}{N_{w}}{\sum}_{i = 1}^{N_{w}}E_{i}$ or the loss or the outputlayer in a current sample window, where N_(W) represents a size of asliding window; if E_(i)>ε is met after an i^(th) training, addingneurons from hidden layer 1, i.e., layer 2 of the dynamic neuralnetwork, l=2; initializing a weight Θ_(new) of the newly added neuronswith a random number, and updating the number of neurons in the l^(th)layer of the dynamic neural network and the weight matrix during ani+1^(th) training:n _(l)(i+1)=n _(l)(i)+1Θ^((l))(i+1)=[Θ^((l))(i), Θ_(new)] after adding neurons to the currentl^(th) layer (1<l<L), if after continuous N_(w) training, a loss changerate of the output layer of the dynamic neural network is less than athreshold${\eta_{3}:\frac{1}{N_{w}}\left( {E_{({i - N_{w}})} - E_{i}} \right)} < {\eta_{3}{and}{}E_{i}} > \varepsilon$is met, going to a next hidden layer for neuron increase judgment, andending structure increase judgment when l=L; Step 2.6: after stoppingadding neurons to the hidden layers of the dynamic neural network,performing structure pruning judgment; taking a hidden layer node outputα^((l))=(α^((l)) ₁, α^((l)) ₂, . . . , α^((l)) _(n) _(l) )^(T)∈R^(n)^(l) as a comparison serial and the dynamic neural network output o=(o₁,o₂, . . . , o_(n) _(l) ) as a reference serial, calculating acorrelation coefficient ξ^(l) _(n) between $\begin{matrix}{a^{(l)} = {{\left( {a_{1}^{(l)},a_{2}^{(l)},\ldots,a_{n_{l}}^{(l)}} \right)^{T} \in {R^{n_{l}}{and}o}} = {\left( {o_{1},o_{2},\ldots,o_{n_{l}}} \right):}}} & \end{matrix}$${\xi_{n}^{l}(k)} = \frac{{\min\limits_{n}\min\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}} + {\rho\max\limits_{n}\max\limits_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}{{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘} + {\rho\max\limits_{n}\max_{k}{❘{{o(k)} - {a_{n}^{(l)}(k)}}❘}}}$where ρ∈(0, +∞) represents a distinguishing coefficient, and ξ^(l)_(n)(k)(k=1,2, . . . , N) represents a grey relation serial; calculatinga relativity using the average value method:$\gamma_{n}^{l} = {\frac{1}{N}{\sum}_{k = 1}^{N}{\xi_{n}^{l}(k)}}$ whereγ^(l) _(n)∈[0,1] represents the relativity; comparing the relativityγ^(l) _(n) between various neurons in the l^(th) layer, deleting aneuron with a minimum relativity, updating the number n_(l) of theneurons in the l^(th) layer of the dynamic neural network and the weightmatrix Θ^((l)); if the loss of the output layer of the dynamic neuralnetwork is increased too much so that E_(i+1)−E_(i)>η₄ after a certainneuron in the l^(th) layer (1<l<L) is deleted, where η₄>0 represents anerror floating threshold, withdrawing the deleting operation in theprevious step and going to a structure adjustment judgment of the nexthidden layer, and ending structure pruning judgment when 1=L; Step 2.7:if the error meets a given accuracy requirement, ending the training,and saving the weight matrix and bias parameter matrix of the dynamicneural network; and if the error does not meet the given accuracyrequirement, proceeding the iterative training until the accuracyrequirement is met or the specified iteration number is reached; Step2.8: based on the test sample, checking the currently trained dynamicneural network, and calculating a test error; Step 3: constructing adynamic neural network controller: encapsulating the trained dynamicneural network as a variable cycle engine controller, and normalizingthe deviation Δn_(h), between the desired value and the actual value ofthe relative rotary speed of high pressure rotor n_(h) of the controlledvariable and the deviation Δπ between the desired value and the actualvalue of the pressure ratio π of the variable cycle engine and thentaking the same as input parameters of the dynamic neural networkcontroller; and inversely normalizing output parameters of the dynamicneural network and then taking the control variables of the variablecycle engine, to realize intelligent control of the variable cycleengine.